The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial
optimization problem in logistics. Quantum and hybrid quantum-classical
algorithms are expected to show an advantage over their classical counterparts
in obtaining approximate solutions for optimization problems. We have recently
proposed a hybrid approach to the one dimensional BPP in which a quantum
annealing subroutine is employed to sample feasible solutions for single
containers. From this reduced search space, a classical optimization subroutine
can find the solution to the problem. With the aim of going a step further in
the evaluation of our subroutine, in this paper we compare the performance of
our procedure with other classical approaches. Concretely we test a random
sampling and a random-walk-based heuristic. Employing a benchmark comprising 18
instances, we show that the quantum approach lacks the stagnation behaviour
that slows down the classical algorithms. Based on this, we conclude that the
quantum strategy can be employed jointly with the random walk to obtain a full
sample of feasible solutions in fewer iterations. This work improves our
intuition about the benefits of employing the scarce quantum resources to
improve the results of a diminishingly efficient classical strategy.Comment: 8 pages, 2 figures, submitted to the IEEE Symposium Series On
Computational Intelligence 202